Machine vision uses a processing computer to recognize certain aspects of a visual field. A signal indicative of the visual field is processed to extract the information it represents. The processing is often effected using elements and filters that are modelled after the human visual system. Many of these devices determine energy concentration in the local spectrum to determine a particular orientation and frequency.
Machine vision uses these techniques for various recognition tasks including recognition of textures, other two dimensional patterns such as fingerprint recognition, object recognition (e.g. faces), and more.
Pyramid filters define a special kind of image filters which decompose the image into several successively smaller, lower resolution images which are band pass and/or low pass components of the original image. The term "pyramid" is based on a nickname that arises from the way that the filtered images look after application of such a filtering scheme. Original images are successively filtered and subsampled to obtain successively smaller images. The resulting set of images is called a pyramid based on appearance. Each adjacent level of the pyramid produces an output indicative of the details that were lost in the formation thereof. In pyramid processing, the resolution of the original image is successively reduced in the coarser smaller images. This is modelled after the way the human visual system operates. Human eyes simultaneously see details at close range and coarse features at a distance. The Pyramid processing breaks down the image into both these detailed and coarse portions. Each Pyramid output represents information about the image.
The Gaussian pyramid is a collection of low pass images, and the Laplacian pyramid is composed of band pass images. These filtering schemes are used to select out spatial frequencies to produce coefficients which characterize the local image area. The Laplacian pyramid is one particularly computationally efficient and compact pyramid filtering scheme.
Different varieties of these pyramids can be used to achieve different kinds of orientation selectivity, and the literature includes teachings of how to form the pyramidal filters including the "Gabor" filters and "Laplacian" Pyramidal filters.
An oriented filter primarily passes information at a specific orientation matching the orientation of the filter. The dominant orientation of an image can be obtained by applying a number of different oriented filters to the image. The image orientation is most closely matched to the orientation of the oriented filter which produces the maximum output. One approach to finding orientation of an image is to simply apply many versions of the same filter, each of which differs from the others by a small rotational angle.
Another possibility is described by Freeman and Adelson in "The Design and Use of Steerable Filters". A steerable filter is formed from a plurality of fixed-orientation "basis filters". The outputs of the basis filters are combined and interpolated between. Freeman describes how to determine a correct filter set to find the response of a filter of arbitrary orientation without explicitly applying that filter.
All angularly-band-limited filters are steerable given enough basis filters, (see Freeman & Adelson). Other pyramids which could be used according to the present invention include the Burt/Adelson pyramid or the filter-subtract-decimate ("FSD") pyramid, for example.
Orientation analysis is an important task in machine vision. The orientation strength along a particular direction .theta. is referred to herein as the "oriented energy" E(.theta.). The "dominant" orientation and the strength of the dominant orientation are estimated from this information. This information can be used to form a "orientation map" which has line lengths which are proportional to the strength S of the orientation, and a phase which indicates the direction .theta..sub.d.
It is an object of the present invention to define a system which can determine orientation and phase information from images containing multi-dominant local orientations.
It is another object of the present invention to provide a system which obtains both orientation magnitude information and orientation phase information in a new and novel way.
This is done according to the present invention by obtaining both rotation variant and rotation invariant information. In one embodiment, this is done by a DFT technique.